El presente artículo evalúa el contenido del informe de auditoría —para caso de Colombia, informe del revisor fiscal— en las empresas cotizadas en la bolsa de valores de Colombia en el periodo 2010-2021, por medio de la metodología Text Mining, que posibilitó el análisis del texto incluido en 647 informes de revisor fiscal, identificando los términos más recurrentes y su importancia dentro del informe, a través del estadístico de frecuencia de términos y nubes de palabras. Con lo anterior, se encontró que los informes del revisor fiscal siguen un formato estándar que, sin embargo, incluyen temas relativos al proceso de auditoría e información adicional a través de los párrafos de énfasis y otros asuntos, mostrando que las palabras o términos que más se usan dentro de los informes son “financiero”, “auditoría” y “opinión”, pero en los párrafos de revelaciones adicionales como párrafos de énfasis y cuestiones clave se incorporan otros temas como procedimientos, deterioro, cartera, evolución y activos para hacer referencia a la información adicional de evaluación de los auditores diferente a solo emitir una opinión de auditoría.

Cited by 2Open access

Text mining

Kolbinger, O. · mediaTUM (Technical University of Munich)

Cited by 0

Text Mining

Thomas Mandl · Advances in information quality and management · 2014

Cited by 3

Text Mining

Chengbin Wang, Xiaogang Ma · Encyclopedia of earth sciences series/Encyclopedia of earth sciences · 2021

Cited by 1Open access

Text Mining

Max Bramer · Undergraduate topics in computer science · 2020

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Text Mining

Reda Alhajj, Jon Rokne · 2018

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Text Mining

2007

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Text mining

Shubhada Nagarkar, Rajendra Kumbhar · Library Review · 2015

Purpose – The purpose of this paper was to analyse text mining (TM) literature indexed in the Web of Science (WoS) under the “Information Science Library Science” subcategory. More specifically, it analyses the chronological growth of TM literature, and the major countries, institutions, departments and individuals contributing to TM literature. Collaboration in TM research is also analysed. Design/methodology/approach – Bibliographic and citation data required for this research were retrieved from the WoS database. TM being a multidisciplinary field, the search was restricted to “Information Science Library Science” subcategory in the WoS. A comprehensive query statement covering all synonyms of “text mining” was prepared using the Boolean operator “OR”. Microsoft Excel and HistCite software were used for data analysis. Pajek and VoSviewer were used for data visualization. Findings – It was found that USA is the major producer of TM research literature, and the highest number of papers were published in the Journal of The American Medical Informatics . Columbia University ranked first both in number of articles and citations received in the top ten institutes publishing TM literature. It was also observed that six of the top ten subdivisions of institutions are either from medicine or medical informatics or biomedical information. H.C. Chen and C. Friedman were seen to be the most prolific authors. Research limitations/implications – The paper analyses articles on TM published during 1999-2013 in WoS under the subcategory Information Science Library Science’. Originality/value – The paper is based on empirical data exclusively gathered for this research.

Cited by 23

Text Mining

Liddy, Elizabeth D. · 2000

Text mining is the process of analyzing naturally occurring text for the purpose of discovering and capturing semantic information for insertion and storage in what I'll call a Knowledge Organization Structure (KOS) with the ultimate goal of enabling knowledge discovery via either textual or visual access for use in a wide range of significant applications.

Cited by 0

Text mining

2010

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Text Mining

Paul Rooprai, Nayaar Islam, Jean-Paul Salameh et al. · OSF Preprints (OSF Preprints)

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